Scalable Parallel Flow Visualization Using 3D Line Integral Convolution for Large Scale Unstructured Simulation Data

Simple item page
dc.contributor.authorLiao, Yangguangen_US
dc.contributor.authorMatsui, Hiroakien_US
dc.contributor.authorKreylos, Oliveren_US
dc.contributor.authorKellogg, Louiseen_US
dc.contributor.editorChilds, Hank and Frey, Steffenen_US
dc.date.accessioned2019-06-02T18:25:46Z
dc.date.available2019-06-02T18:25:46Z
dc.date.issued2019
dc.description.abstractTo address the need of highly efficient and scalable parallel flow visualization methods, we developed a flow visualization system for large unstructured simulation data using parallel 3D line integral convolution (LIC). The main consideration for a parallel LIC implementation is a trade-off between the additional memory cost of replicating cells at sub-domain boundaries, or the communication cost of exchanging those data among computation nodes. To improve scalability, we introduce a load-balancing scheme that partitions datasets based on estimated LIC computation time. We also introduce a data-driven sub-domain extension scheme that determines which external cells at sub-domain boundary need to be added based on current boundary cells, which reduces memory overhead because the same visual quality can be achieved with a significantly smaller number of replicated external cells. We evaluate our visualization method by first comparing its parallel scalability to traditional integral field lines methods. Next, we compare our cost-driven domain decomposition method to existing methods to verify that ours leads to more balanced computation and improved scalability. Finally, we compare our data-driven sub-domain expansion method to traditional layer-based expansion methods in terms of memory overhead and visual quality. We conclude that our parallel 3D LIC method is an efficient and scalable approach to visualization of large and complex 3D vector fields.en_US
dc.description.sectionheadersSession 1
dc.description.seriesinformationEurographics Symposium on Parallel Graphics and Visualization
dc.identifier.doi10.2312/pgv.20191106
dc.identifier.isbn978-3-03868-079-6
dc.identifier.issn1727-348X
dc.identifier.pages17-25
dc.identifier.urihttps://doi.org/10.2312/pgv.20191106
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/pgv20191106
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectScientific visualization
dc.subjectSoftware and its engineering
dc.subjectParallel programming languages
dc.subjectComputing methodologies
dc.subjectConcurrent algorithms
dc.titleScalable Parallel Flow Visualization Using 3D Line Integral Convolution for Large Scale Unstructured Simulation Dataen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
017-025.pdf
Size:
8.32 MB
Format:
Adobe Portable Document Format
Download
Collections
EGPGV19: Eurographics Symposium on Parallel Graphics and Visualization